AI-Powered Material Master Cleansing: What It Can and Cannot Do

The Promise of AI in Material Master Cleansing

Manually cleansing thousands of material records is slow, expensive and inconsistent. AI changes this — a model trained on material descriptions can parse free-text fields, identify the noun and modifiers, extract attribute values and rewrite records in a standard format at scale.

What AI Does Well

AI excels at parsing unstructured descriptions to identify the item type, extracting attributes like size, material, standard and pressure rating, reformatting into a consistent noun-modifier-attribute structure, flagging likely duplicates for human review, and suggesting UNSPSC codes based on material classification.

Where Human Review Remains Essential

AI confidence scores drop sharply when descriptions are very short, use local abbreviations, or contain conflicting information. A record reading 'VLV 2 GATE SS' leaves ambiguity on pressure rating, end connection and standard — a specialist resolves this; AI guesses. Getting the wrong attribute into your ERP costs more to fix than taking the time to review it properly.

The VCode Approach: AI-Assisted, Human-Verified

VCode uses AI to do the heavy lifting — extracting, structuring and formatting — and material master specialists to review every output before delivery. High-confidence records move through quickly; ambiguous ones get the attention they need. The result is cleansed data you can trust.